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In September 2018, IBM introduced a recent product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts access patterns from executing SQL statements, uses computer learning algorithms to select greatest patterns and passes this counsel to the Db2 question optimizer for spend by passage of subsequent statements.computing device researching on the IBM z Platform
In can furthermore of 2018, IBM announced edition 1.2 of its desktop researching for z/OS (MLz) product. here is a hybrid zServer and cloud software suite that ingests efficiency data, analyzes and builds models that characterize the fitness reputation of a variety of indications, displays them over time and provides actual-time scoring capabilities.
several facets of this product providing are geared toward aiding a community of model builders and executives. for instance:
This desktop researching suite become at the beginning geared toward zServer-based mostly analytics functions. one of the most first glaring choices turned into zSystem performance monitoring and tuning. paraphernalia management Facility (SMF) information that are automatically generated by the working system deliver the raw statistics for system aid consumption equivalent to apposite processor usage, I/O processing, reminiscence paging and the like. IBM MLz can bring together and store these statistics over time, and build and educate models of paraphernalia behavior, score these behaviors, determine patterns now not conveniently foreseen by humans, ameliorate key efficiency warning signs (KPIs) after which feed the model results again into the system to move device configuration adjustments that may enrich efficiency.
The subsequent step was to establish in force this suite to resolve Db2 efficiency facts. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop studying technology to Db2 operational information to profit an figuring out of Db2 subsystem health. it could actually dynamically build baselines for key efficiency indicators, deliver a dashboard of these KPIs and provides operational staff precise-time perception into Db2 operations.
while widespread Db2 subsystem efficiency is an well-known aspect in gauge application fitness and performance, IBM estimates that the DBA guide group of workers spends 25% or more of its time, " ... fighting entry direction complications which occasions performance degradation and repair influence.". (See Reference 1).AI involves Db2
agree with the plight of modern DBAs in a Db2 ambiance. In latest IT world they should assist one or greater massive information applications, cloud utility and database functions, utility installation and configuration, Db2 subsystem and software efficiency tuning, database definition and administration, catastrophe recovery planning, and more. question tuning has been in being considering that the origins of the database, and DBAs are continually tasked with this as neatly.
The coronary heart of question direction evaluation in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the data, experiences the areas of the objects to live accessed and develops a list of candidate records entry paths. These entry paths can encompass indexes, desk scans, numerous desk associate methods and others. within the facts warehouse and great information environments there are usually additional decisions available. One of these is the being of abstract tables (on occasion referred to as materialized question tables) that comprise pre-summarized or aggregated facts, for that reason enabling Db2 to remain away from re-aggregation processing. an extra election is the starjoin access path, usual within the information warehouse, the position the order of desk joins is changed for performance motives.
The Optimizer then stories the candidate entry paths and chooses the access route, "with the lowest charge." cost during this context means a weighted summation of aid utilization including CPU, I/O, reminiscence and different materials. at last, the Optimizer takes the lowest can charge entry path, retailers it in reminiscence (and, optionally, in the Db2 directory) and starts off access route execution.
large statistics and information warehouse operations now encompass utility suites that permit the business analyst to fabricate spend of a graphical interface to construct and exploit a miniature information model of the information they are looking to analyze. The applications then generate SQL statements based on the clients’ requests.
The hardship for the DBA
with a purpose to carry out first rate analytics on your dissimilar facts shops you necessity a marvelous knowing of the information necessities, an knowing of the analytical capabilities and algorithms available and a high-efficiency facts infrastructure. alas, the number and site of statistics sources is increasing (each in dimension and in geography), facts sizes are growing, and functions continue to proliferate in number and complexity. How may noiseless IT managers advocate this environment, notably with probably the most experienced and age body of workers nearing retirement?
consider additionally that a huge fragment of reducing the full cost of ownership of these methods is to entangle Db2 applications to evade faster and more correctly. This continually interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information throughout the community. because it is often complex to even identify which applications might edge from efficiency tuning, one approach is to automate the detection and correction of tuning concerns. this is the position computing device learning and synthetic intelligence can furthermore live used to exquisite impact.Db2 12 for z/OS and synthetic Intelligence
Db2 edition 12 on z/OS makes spend of the computer researching amenities outlined above to accumulate and shop SQL query textual content and access path details, in addition to actual efficiency-linked historical suggestions reminiscent of CPU time used, elapsed instances and outcomes set sizes. This providing, described as Db2 AI for z/OS, analyzes and outlets the facts in computing device getting to know fashions, with the model evaluation results then being scored and made obtainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then spend the model scoring data as enter to its access direction alternative algorithm.
The result should live a reduction in CPU consumption as the Optimizer uses model scoring input to choose more advantageous access paths. This then lowers CPU charges and speeds utility response instances. a major skills is that the usage of AI software does not require the DBA to fill data science potential or deep insights into query tuning methodologies. The Optimizer now chooses the most fulfilling entry paths primarily based not most efficacious on SQL question syntax and records distribution records however on modelled and scored historic efficiency.
This can live principally vital in case you shop facts in varied locations. as an instance, many analytical queries in opposition t substantial records require concurrent access to unavoidable information warehouse tables. These tables are often referred to as dimension tables, and that they accommodate the facts facets constantly used to manage subsetting and aggregation. as an example, in a retail atmosphere believe a table known as StoreLocation that enumerates each shop and its location code. Queries towards save earnings information may furthermore wish to combination or summarize income by means of region; therefore, the StoreLocation desk will live used by means of some substantial records queries. in this environment it is unprejudiced to snitch the dimension tables and duplicate them regularly to the great facts software. in the IBM world this position is the IBM Db2 Analytics Accelerator (IDAA).
Now deem about SQL queries from both operational functions, data warehouse clients and massive information enterprise analysts. From Db2's viewpoint, any these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should surely live directed to entry the StoreLocation table within the warehouse. on the other hand, the query from the business analyst towards substantial data tables should likely access the copy of the desk there. This results in a proliferations of abilities access paths, and more labor for the Optimizer. luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to fabricate sensible access path choices.the passage it Works
The sequence of events in Db2 AI for z/OS (See Reference 2) is frequently perquisite here:
There are additionally various user interfaces that provide the administrator visibility to the popularity of the collected SQL remark efficiency data and mannequin scoring.summary
IBM's machine learning for zOS (MLz) offering is getting used to extremely marvelous upshot in Db2 version 12 to ameliorate the performance of analytical queries as well as operational queries and their associated functions. This requires management consideration, as you necessity to examine that your enterprise is prepared to consume these ML and AI conclusions. How will you measure the fees and merits of the usage of computer learning? Which IT guide body of workers ought to live tasked to reviewing the influence of mannequin scoring, and maybe approving (or overriding) the consequences? How will you overview and justify the assumptions that the software makes about access path choices?
In different phrases, how well were you alert your data, its distribution, its integrity and your latest and proposed access paths? this will check where the DBAs spend their time in helping analytics and operational utility performance.
# # #
John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 software efficiency with machine getting to know"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-enhance-ibm-db2-software-efficiency-with-ma
Db2 AI for z/OShttps://www.ibm.com/aid/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
Over on the IBM blog, IBM Fellow Hillary Hunter writes that the company anticipates that the area’s volume of digital records will exceed forty four zettabytes, an astonishing number. As companies initiate to realize the great, untapped lore of statistics, they should locate a method to exploit it. Enter AI.
IBM has worked to construct the industry’s most finished records science platform. integrated with NVIDIA GPUs and application designed specially for AI and the most statistics-intensive workloads, IBM has infused AI into offerings that customers can entry in spite of their deployment model. these days, they snitch the subsequent step in that event in announcing the next evolution of their collaboration with NVIDIA. They blueprint to leverage their recent data science toolkit, RAPIDS, across their portfolio in order that their shoppers can raise the performance of laptop gaining lore of and facts analytics.
Plans to promote GPU-accelerated machine studying encompass:
IBM and NVIDIA’s shut collaboration through the years has helped main corporations and companies any over ply one of the world’s greatest problems,” observed Ian Buck, vp and customary supervisor of Accelerated Computing at NVIDIA. “Now, with IBM taking talents of RAPIDS open-source libraries announced today by means of NVIDIA, GPU accelerated laptop studying is coming to statistics scientists, helping them resolve huge information for insights quicker than ever feasible before. Recognizing the computing vigor that AI would need, IBM was an early recommend of records-centric programs. This method led us to carry the GPU-fitted pinnacle equipment, the world’s most powerful supercomputer, and already researchers are seeing colossal returns. earlier within the yr, they verified the skills for GPUs to hasten up computer learning after they showed how GPU-accelerated computer getting to know on IBM power techniques AC922 servers set a new hasten listing with a 46x improvement over previous consequences.
as a result of IBM’s dedication to bringing accelerated AI to users throughout the know-how spectrum, live they users of on-premises, public cloud, deepest cloud, or hybrid cloud environments, the company is positioned to convey RAPIDS to clients in spite of how they necessity to entry them.
Hillery Hunter is an IBM Fellow and CTO of Infrastructure within the IBM Hybrid Cloud business. earlier than this function, she served as Director of Accelerated Cognitive Infrastructure in IBM analysis, main a group doing go-stack (hardware through software) optimization of AI workloads, producing productiveness breakthroughs of 40x and better which fill been transferred into IBM product offerings. Her technical interests fill at any times been interdisciplinary, spanning from silicon know-how via gadget software, and he or she has served in technical and leadership roles in recollection know-how, techniques for AI, and different areas. She is a member of the IBM Academy of expertise.
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Ricardo Balduino and Tim BohnEarly Flight, Creative Commons Introduction
As they described in fragment 1 of this series, their objective is to back foretell the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They spend historical flights data and historical weather data to fabricate predictions for upcoming flights.
Over the course of this four-part series, they spend different platforms to back us with those predictions. Here in fragment 2, they spend the IBM SPSS Modeler and APIs from The Weather Company.Tools used in this spend case solution
IBM SPSS Modeler is designed to back learn patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a orbit of advanced algorithms and analysis techniques, including text analytics, entity analytics, determination management and optimization to deliver insights in near real-time. For this spend case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.
We furthermore used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.A proposed solution
In this blog, they propound one possible solution for this problem. It’s not meant to live the only or the best possible solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They encourage the readers to try and arrive up with different solutions, and provide us with your feedback for future blogs.Business and data understanding
The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.
Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge any the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in figure 1.Figure 1 — gathering and preparing flights data in IBM SPSS Modeler
Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in figure 2.Figure 2 — gathering and preparing weather data in IBM SPSS Modeler
Combined flights and weather data — To each flight in the first data set, they added two recent columns: inception and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in figure 3 are placeholders for the diagrams in Figures 1 and 2 above.Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation
We iteratively performed the following steps until the desired model qualities were reached:
· Prepare data
· effect modeling
· Evaluate the model
Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.Figure 4 — iterations: prepare data, evade models, evaluate — and carry out it again First iteration
To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took charge of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)Figure 5 — imbalanced data establish in their input data set
This is an challenging spend case, and often a difficult one to solve, due to the imbalanced data it presents, as seen in figure 5. By “imbalanced” they weigh in that there were far more non-cancelled flights in the historical data than cancelled ones. They will debate how they dealt with imbalanced data in the following iteration.
Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, inception and destination airport codes, and weather conditions), and which one was the target to live generated by the model (i.e. foretell the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.
The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to evade multiple models at once and preview their outputs, such as the belt under the ROC curve, as seen in figure 6.Figure 6 — models output provided by the Auto Classifier node
That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to spend the Random Trees model since the initial analysis showed it has the best belt under the curve as compared to the other models in the list.Second iteration
During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model labor more effectively.Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE
In figure 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.
As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less supine to overfitting, which gives a higher likelihood of repeating the identical test results when you spend recent data, that is, data that was not fragment of the original training and testing data sets. Another edge of this method — in particular for their spend case — is its faculty to ply imbalanced data.
Since in this spend case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in figure 8. The table shows the precision achieved by the model during training.Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights
In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was remedy most of the time, but furthermore made wrong predictions about 4–5% of the time (false negatives and unfounded positives).
That was the precision given by the model using the training data set. This is furthermore represented by the ROC curve on the left side of figure 9. They can see, however, that the belt under the curve for the training data set was better than the belt under the curve for the testing data set (right side of figure 9), which means that during testing, the model did not effect as well as during training (i.e. it presented a higher rate of errors, or higher rate of unfounded negatives and unfounded positives).Figure 9 — ROC curves for the training and testing data sets
Nevertheless, they decided that the results were noiseless marvelous for the purposes of their discussion in this blog, and they stopped their iterations here. They encourage readers to further refine this model or even to spend other models that could decipher this spend case.Deploying the model
Finally, they deployed the model as a ease API that developers can call from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can furthermore call The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and fabricate the prediction.
A typical scoring endpoint provided by the Watson Machine Learning service would recognize like the URL shown below.
https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>
By passing the expected JSON body that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to live cancelled or not. This is seen in figure 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester utensil available in a web browser.Figure 10 — actual request URL, JSON body, and response from scoring endpoint
Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would live 88.8% likely to live cancelled, based on forecast weather conditions.Conclusion
IBM SPSS Modeler is a powerful utensil that helped us visually create a solution for this spend case without writing a single line of code. They were able to result an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.Resources
The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can furthermore find instructions on how to download IBM SPSS Modeler, entangle a key for The Weather Channel APIs, and much more.
Royalty-free I3C; CFET parasitic variation modeling; Intel funds analog IP generation.
The MIPI Alliance released MIPI I3C Basic v1.0, a subset of the MIPI I3C sensor interface specification that bundles 20 of the most commonly needed I3C features for developers and other standards organizations. The royalty-free specification includes backward compatibility with I2C, 12.5 MHz multi-drop bus that is over 12 times faster than I2C supports, in-band interrupts to allow slaves to notify masters of interrupts, dynamic address assignment, and standardized discovery.
Efinix will expand its product offering, adding a 200K logic component FPGA to its lineup with the Triton T200. The T200 targets AI-driven products, and its architecture has enough LEs, DSP blocks, and on-chip RAM to deliver 1 TOPS for CNN at INT8 precision and 5 TOPS for BNN, according to Efinix CEO Sammy Cheung. The company furthermore released samples of its Trion T20 FPGA.
Faraday Technology released multi-protocol video interface IP on UMC 28nm HPC. The Multi-Protocol Video Interface IP solution supports both transmitter (TX) and receiver (RX). The transmitter allows for MIPI and CMOS-IO combo solutions for package cost reduction and flexibility, while the receiver combo PHY includes MIPI, LVDS, subLVDS, HiSPi, and CMOS-I/O to advocate a diversified orbit of interfaces to CMOS image sensors. Target applications include panel and sensor interfaces, projectors, MFP, DSC, surveillance, AR and VR, and AI.
Analog utensil and IP maker Movellus closed a second round of funding from Intel Capital. Movellus’ technology automatically generates analog IPs using digital implementation tools and gauge cells. The company will spend the funds to expand its customer ground and to augment its portfolio of PLLs, DLLs and LDOs for spend in semiconductor and system designs at advanced process nodes.
Imec and Synopsys completed a comprehensive sub-3nm parasitic variation modeling and retard sensitivity study of complementary FET (CFET) architectures. The QuickCap NX 3D realm solver was used by Synopsys R&D and imec research teams to model the parasitics for a variety of device architectures and to identify the most faultfinding device dimensions and properties, which allowed for optimization of CFET devices for better power/performance trade-offs.
Credo utilized Moortec’s Temperature Sensor and Voltage Monitor IP to optimize performance and augment reliability in its latest generation of SerDes chips. Moortec’s PVT sensors are utilized in any Credo gauge products which are being deployed on system OEM linecards and 100G per lambda optical modules. Credo cited ease of integration and reduced time-to-market and project risk.
Wave Computing selected Mentor’s Veloce Strato emulation platform for functional verification and validation of its latest Dataflow Processor Unit chip designs, which will live used in the company’s next-generation AI system. Wave cited capacity and scaling advantages, breadth of virtual spend models, reliability, and determinism as behind the choice.
MaxLinear adopted Cadence’s Quantus and Tempus timing signoff tools in developing the MxL935xx Telluride device, a 400Gbps PAM4 SoC using 16FF process technology. MaxLinear estimated they got 2X faster multi-corner extraction runtimes versus single-corner runs and 3X faster timing signoff flow.
The European Processor Initiative selected Menta as its provider of eFPGA IP. The EPI, a collaboration of 23 partners including Atos, BMW, CEA, Infineon and ST, has the objective of co-designing, manufacturing and bringing to market a system that supports the high-performance computing requirements of exascale machines.Jesse Allen (all posts)Jesse Allen is the lore headquarters administrator and a senior editor at Semiconductor Engineering.
Microsoft announced on Monday that recent tools fill been released to back further extend the compatibility and interoperability of Office Open XML (OOXML) document formats used in Microsoft Office 2007.
The recent tools are being developed by various open source projects. In addition, the Fraunhofer Fokus research group is working on a future "test library and validation tool" that will check document formats to survey how well they comply with ISO/IEC 29500 and ECMA-376, which are OOXML-based international standards. Microsoft is a partner in the validation utensil effort, which was announced in late February.
One of the open source projects releasing a recent utensil is Apache POI, which works to fabricate OOXML files readable in Java-based applications. On Monday, Apache POI 3.5 beta 5 was released at the Apache POI Web site, along with a software evolution kit. This latest release adds "improved support" for .DOCX (Word) and .PPTX (PowerPoint) file formats, as well as "extended support" for the .XLSX (Excel) file format, according to a Microsoft announcement. Microsoft first began collaborating with the Apache POI project back in March of ultimate year.
On Friday, MindTree and Microsoft released the Open XML Document Viewer v1.0 application. This browser plug-in, available at the CodePlex open source project site, allows Microsoft Office 2007 documents to live read in a Web browser. The Open XML Document Viewer, which translates OOXML-based files to HTML, now supports the Opera browser on both Windows and Linux. Other supported browsers include Firefox and Internet Explorer versions 7 and 8.
Microsoft and Dialogika fill enhanced an Office Binary to Open XML Translator application by adding advocate for .XLS and .PPT files. This application lets the user translate Office binary files into OOXML and OpenDocument Format (ODF) files. The facet III final version of the translator was released on SourceForge in late April.
Finally, the Open XML-ODF Translator add-in for Microsoft Office got some improvements with version 3.0, which was released in late March on SourceForge. Microsoft supported ODF 1.1 with this translator release.
Native advocate for ODF 1.1 is now fragment of Microsoft Office 2007 Service Pack 2, which was released in late April. However, the quality of that advocate has sparked an open spat among OASIS Technical Committee members who are currently overseeing the ODF international standard.
A blog entry by Rob Weir, IBM's chief ODF architect and chair of the ODF Technical Committee at OASIS, accused Microsoft of either incompetence or sabotage by not supporting an ODF namespace convention that helps translate formulas in spreadsheets between applications. In response, Gray Knowlton, a Microsoft group product manager, called for Weir to "step down as chairman." Microsoft and IBM noiseless fill some substandard blood left over from a contentious ISO/IEC OOXML standardization process and both are now participants in the OASIS ODF standards effort.
Microsoft's Doug Mahugh, lead standards professional on the Office interoperability team, explained in his blog that the ODF gauge doesn't specify the code-handing details for formulas sufficiently enough. He claimed that even IBM's Lotus Symphony spreadsheet has a problem translating formulas to other ODF-based spreadsheets, such as Sun's OpenOffice.org. In a later blog entry, Mahugh said that ODF document changes aren't being supported in Microsoft Word's ODF implementation OOXML because of technical issues and unclear ODF documentation in the ODF specification, among other details.
"Tracked changes are essential to document collaboration, and formulas are the essence of spreadsheets. Microsoft's failure to advocate either in SP2 is revealing with regard to its advocate for real-world interoperability," stated Marino Marcich, managing director of the ODF Alliance, an industry trade group promoting ODF, in a released statement.
The upshot of these spats, according to a Burton Group blog, is that there are noiseless major compatibility problems between the ODF and OOXML document formats. The blog emphasized that enterprises should stick with the document formats they currently spend in their office productivity software until such kinks entangle worked out. The blog furthermore notable that ODF 1.2, when it's released, will likely fill an Open Formula syntax that will decipher the current impasse.
Kurt Mackie is senior news producer for the 1105 Enterprise Computing Group.
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